Instructions to use ProbeX/Model-J__ResNet__model_idx_0785 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0785 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0785") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0785") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0785", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 95367b82b2f667e72f706ba795bdfbb02f1cd15cd748f4f9464c2625db3b3540
- Size of remote file:
- 171 MB
- SHA256:
- 94951fccc1efae566e8614bef5e77010143424c3bd00c2ec3a4ec605813beca0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.